Product release method and system, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a product release method and system, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a data source which is input by a user and is used for publishing a target product; sending a product information generation request to a server, so that the server generates preset release information of the target product based on the user information of the user and the data source; initializing a publishing configuration interface by taking preset publishing information as an initial value of the corresponding to-be-published information; in response to a release configuration operation executed in the release configuration interface, obtaining to-be-released information, a target release platform and / or a user-defined product release rule configured by a user; and sending a product release request to a server, so that the server executes a product release operation of the target product based on the to-be-released information configured by the user, the target release platform and / or the user-defined product release rule. The method can improve the product publishing efficiency and the matching degree between the published product and the user.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular, to a product release method, a product release system, an electronic device, a storage medium, and a computer program product. Background Art

[0002] Product release refers to submitting key product information to a target platform according to the requirements of the target platform, so that the target site can display the product to users. In the prior art, when releasing a product, the product publisher needs to prepare data by themselves, including title drafting, detailed description design, picture generation, etc., and fill in forms and a series of operations, resulting in low release efficiency. Moreover, the product release method in the prior art has relatively high requirements for merchants, and the quality of the released product depends on the ability of the publisher, and the released product is unstable.

[0003] It can be seen that the product release method in the prior art urgently needs to be improved. Summary of the Invention

[0004] Embodiments of the present application provide a product release method, which can effectively improve the product release efficiency and quality.

[0005] Correspondingly, embodiments of the present application further provide a product release platform, an electronic device, a storage medium, and a computer program product to ensure the implementation and application of the above product release method.

[0006] To solve the above problems, embodiments of the present application disclose a product release method, which is applied to a client, and the method includes: Obtain a data source input by a user for releasing a target product; Send a product information generation request to a server, so that the server generates preset release information of the target product based on the user information of the user and the data source; Initialize a release configuration interface with the preset release information as an initial value of corresponding information to be released; In response to a release configuration operation performed on the release configuration interface, obtain information to be released, a target release platform, and / or a custom product release rule configured by the user; Send a product release request to the server, so that the server performs a product release operation of the target product based on the information to be released, the target release platform, and / or the custom product release rule configured by the user.

[0007] To solve the above problems, embodiments of the present application disclose a product release method, which is applied to a server, and the method includes: In response to a product information generation request sent by a client, obtain the user information carried in the product information generation request and the data source for publishing the target product; Based on the user information and the data source, generate the preset publishing information for the target product; Send the preset publishing information to the client, so that the client initializes the publishing configuration interface with the preset publishing information as the initial value of the corresponding information to be published, and in response to the publishing configuration operation performed on the publishing configuration interface, obtain the information to be published, the target publishing platform and / or the custom product publishing rule configured by the user; In response to a product publishing request sent by the client, obtain the information to be published, the target publishing platform and / or the custom product publishing rule configured by the user carried in the product publishing request; Execute the product publishing operation of the target product based on the information to be published, the target publishing platform and / or the custom product publishing rule configured by the user.

[0008] An embodiment of this application also discloses a product publishing system, including a client and a server. Among them, The client is used to execute the product publishing method applied to the client as described above; The server is used to execute the product publishing method applied to the server as described above.

[0009] An embodiment of this application also discloses a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the method as described in the embodiment of this application.

[0010] An embodiment of this application also discloses a computer program product, including a computer program / computer executable instructions. When the computer program / computer executable instructions are executed by a processor in an electronic device, they implement the method as described in the embodiment of this application.

[0011] Compared with the prior art, the embodiments of this application include the following advantages: By obtaining the data source for publishing the target product input by the user on the client side and sending a product information generation request to the server side, the server side generates the preset publishing information of the target product based on the user information of the user and the data source, making the generated product publishing information more in line with the user's preferences; then, the client uses the preset publishing information as the initial value of the corresponding information to be published, initializes the publishing configuration interface, and in response to the publishing configuration operation performed on the publishing configuration interface, obtains the information to be published, the target publishing platform and / or the custom product publishing rule configured by the user; then, sends a product publishing request to the server side, enabling the server side to perform the product publishing operation of the target product based on the information to be published, the target publishing platform and / or the custom product publishing rule configured by the user, realizing fully automated and fast product publishing, and effectively improving the product publishing efficiency. Description of the Drawings

[0012] Figure 1 is one of the step flowcharts of the product publishing method disclosed in the embodiments of the present application; Figure 2 is the schematic diagram of the implementation architecture of the product publishing method disclosed in the embodiments of the present application; Figure 3 is the second of the step flowcharts of the product publishing method disclosed in the embodiments of the present application; Figure 4 is the interaction schematic diagram of the product publishing system disclosed in the embodiments of the present application; Figure 5 is the schematic diagram of the structure of an exemplary device provided by an embodiment of the present application. Detailed Description of the Embodiments

[0013] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0014] The product publishing method disclosed in the embodiments of the present application can be applied to product publishing on websites, such as scenarios of publishing application products in application websites, publishing commodities in e-commerce websites, etc. This method can automatically generate the publishing information of products based on various forms of materials of the products to be published input by the product publisher, based on the text understanding and generation technology of artificial intelligence, combined with the publisher's preferences and the product information of the same high-quality products on the global sites, so as to realize one-key product publishing, not only improving the product publishing efficiency, but also improving the product publishing quality.

[0015] The product publishing method disclosed in the embodiments of the present application is implemented through the cooperation of the client and the server side. As Figure 1 shown, the product publishing method applied to the client includes: Step 102 to Step 110.

[0016] The following will describe the specific implementation manners of each step in conjunction with Figure 2 the schematic diagram of the implementation architecture shown.

[0017] As Figure 2 shown, when implementing the product release method, multiple large models pre-trained with sample data based on different stages and different tasks are preset on the server side. For example, a large model for understanding input information, a large model for category prediction based on the understanding result of the input information, a large model for generating product information based on the understanding result of the input information and the category prediction result, a large model for refining user preference information based on the user's historical data, a large model for generating information to be released, a large model for optimizing the released information based on product release rules, etc. At different stages of product release, by calling the corresponding large model, information understanding, information extraction or generation processing is performed on the input data of this stage to obtain the output data of this stage.

[0018] Step 102, obtain the data source input by the user for releasing the target product.

[0019] Taking the application of the product release method disclosed in the embodiments of the present application to the product release of an e-commerce website as an example, the user refers to a merchant, and the target product is the product to be released. The data source refers to the material for generating the release information of the target product (i.e., the product to be released). In some optional embodiments, the user can input the material for generating the release information of the product to be released, that is, the data source, through the human-computer interaction interface provided by the client. Optionally, the data source includes, but is not limited to, one or more of the following forms: text, picture, hyperlink, product material compression package. Among them, the hyperlink includes: product link; the product material compression package includes, but is not limited to: text file, picture file.

[0020] In the embodiments of the present application, the form, content, and generation method of the data source are not limited. Generally, the data source includes one or more of the product attribute information such as product pictures, product description texts, product keywords, selling points, etc. of the product to be released.

[0021] Step 104, send a product information generation request to the server, so that the server generates the preset release information of the target product based on the user information of the user and the data source.

[0022] Optionally, after the user inputs the data source of the target product, the user can perform a product generation operation through the operation entry set on the client interface.

[0023] In some alternative embodiments, after the client detects that the user performs an operation to start generating a product, it sends a product information generation request to the server of the product release system. The product information generation request carries at least: the user information of the user and the data source. After receiving the product information generation request, the server obtains the user information and the data source carried in the product information generation request, and further generates the release information of the target product based on the user information and the data source.

[0024] Optionally, generating the preset release information of the target product based on the user information of the user and the data source includes: sub-steps S1 to S4.

[0025] Sub-step S1: Perform product information analysis and processing on the data source to obtain the first product information input by the user of the target product.

[0026] Optionally, performing product information analysis and processing on the data source to obtain the first product information input by the user of the target product includes: performing product information analysis and processing operations on the data source to obtain the text and / or pictures input by the user; calling a preset large model to perform information extraction and generation processing on the text and / or pictures to obtain the first product information input by the user of the target product, where the first product information includes one or more of the following: category, title, picture, keyword, description, logistics, attribute.

[0027] Optionally, the data source includes one or more of the following: product description text, product pictures, product links, and product material compressed packages. Performing product information analysis and processing operations on the data source to obtain the text and / or pictures input by the user includes: when the product description text is included in the data source, using the product description text as the first product description text; when the product pictures are included in the data source, using the product pictures as the first product pictures; when the product material compressed package is included in the data source, decompressing the product material compressed package to obtain product material text files and / or product material picture files, and then, obtaining the pictures in the product material picture files as the second product pictures, and obtaining the text in the product material text files as the second product description text; when the product link is included in the data source, obtaining the product pictures in the page associated with the product link as the third product pictures, and obtaining the text in the page associated with the product link as the third product description text; using the first product pictures and / or the second product pictures and / or the third product pictures as the pictures input by the user of the target product, and using the first product description text and / or the second product description text and / or the third product description text as the text input by the user of the target product.

[0028] Among them, the specific implementation manners of decompressing the product material compressed package to obtain product material text files and / or product material picture files, and obtaining the pictures in the product material picture files and the text in the product material text files refer to the prior art and will not be elaborated in the embodiments of the present application; the specific implementation manners of obtaining the product pictures and text in the page associated with the product link refer to the prior art and will not be elaborated in the embodiments of the present application.

[0029] In some optional embodiments, for the data source input by the user, by using data analysis and processing means matching the type of the data source, the text and / or pictures included in the corresponding type of data source for describing the target product can be obtained, and the text and pictures included in all data sources for describing the target product are jointly used as the input data for extracting the preset key information of the target product. Further, a preset large model is called to perform information extraction and generation processing on the input data to obtain the first product information input by the user of the target product.

[0030] Optionally, a prompt word can be generated based on the input data and a first preset prompt word template, and the preset large model is called based on the prompt word to trigger the preset large model to perform information extraction and expansion based on the input data to obtain the preset key information. Among them, the specific implementation manners of the preset large model performing information extraction and expansion based on the input data to obtain the preset key information refer to the prior art and will not be elaborated in the embodiments of the present application.

[0031] Optionally, when extracting key information of different products based on input data, different pre-trained large models fine-tuned based on different training data can be adopted to improve the accuracy of information extraction.

[0032] By calling a pre-trained large model, extract key product information (such as product title, keywords, selling points, category, core attributes, etc.) from pictures, texts, and specified link pages, and output it in a structured manner to obtain the preset key product information of the target product itself, which is used as part of the product information, that is, the first product information. At the same time, it is used to collect key product information of similar products in subsequent steps.

[0033] Optionally, the preset product key information includes, but is not limited to, one or more of the following: product pictures, categories, product titles, product keywords, selling points, attributes.

[0034] Sub-step S2, based on the first product information, obtain the second product information of the popular products of the same model as the target product.

[0035] Optionally, the obtaining of the second product information of the popular products of the same model as the target product based on the first product information includes: encoding one or more of the first product information to obtain an encoding vector of the target product; using a vector matching method to recall products to be matched from a preset product library based on the encoding vector; using a pre-trained large model to score the relevance of the target product and the products to be matched based on the first product information, and screening out the products of the same model as the target product from the products to be matched according to the scoring results; screening the products of the same model based on a preset quality evaluation index to obtain the popular products of the same model as the target product; obtaining the preset product information of the products of the same model as the second product information.

[0036] Among them, the preset product library is set according to the specific application scenario requirements. For example, when the product to be released is a commodity on a cross-border e-commerce website, the preset product library may include the published commodities of global mainstream e-commerce websites.

[0037] In specific implementation, existing text encoding methods in the prior art can be used to encode text information in the first product information, and existing picture encoding methods in the prior art can be used to encode product pictures in the first product information. On the other hand, the same encoding method is used to encode the text and pictures of products in the preset product library respectively.

[0038] In some alternative embodiments, if the first product information includes a product picture, the product picture included in the first product information may first be encoded to obtain the picture encoding vector of the target product. On the other hand, the product pictures of each product (i.e., the product to be matched) in the preset product library are respectively encoded in the same way to obtain the picture encoding vectors of the products to be matched. Then, the similarities between the picture encoding vector of the target product and the picture encoding vectors of the products to be matched are calculated, and the products to be matched whose similarities with the target product meet the preset similarity condition are recalled.

[0039] In some other alternative embodiments, if the first product information includes text information, the text information included in the first product information may first be encoded to obtain the picture encoding vector of the target product. On the other hand, the corresponding text-based product information of each product (i.e., the product to be matched) in the preset product library is respectively encoded in the same way to obtain the text encoding vectors of the products to be matched. Then, the similarities between the text encoding vector of the target product and the text encoding vectors of the products to be matched are calculated, and the products to be matched whose similarities with the target product meet the preset similarity condition are recalled.

[0040] In some other embodiments of the present application, other vector matching methods may also be used to recall the products to be matched from the preset product library based on the encoding vectors, which will not be listed one by one here.

[0041] Next, for the products to be matched recalled from the preset product library, a preset large model is used to score the relevance between the target product and the products to be matched based on the first product information, and a scoring result is obtained. Then, according to the scoring result, the first preset number of products to be matched with the highest relevance to the target product are selected from the similar products as the same-model products of the target product; or, according to the scoring result, the products to be matched whose relevance with the target product meets the preset relevance condition are selected from the similar products as the same-model products of the target product.

[0042] After that, the preset quality evaluation indicators of each same-model product of the target product are calculated respectively, where the value of the preset quality evaluation indicator is positively correlated with the product quality. The preset quality evaluation indicator is determined according to specific application requirements. For example, the preset quality evaluation indicator includes but is not limited to one or more of the following: click-through rate, order volume, and positive review rate. Then, the second preset number of same-model products with the largest preset quality evaluation indicator are selected as the popular same-model products of the target product. And the preset product information of the popular same-model products is obtained as the second product information.

[0043] Sub-step S3: Obtain the user preference information matched by the user based on the user historical data associated with the user information.

[0044] Optionally, the user preference information includes one or more of the following: shipping address preference, product price preference, logistics preference, product title preference, delivery cycle preference. Among them, the shipping address preference includes, but is not limited to: common shipping address, default shipping address; the product price preference is used to indicate whether the user prefers high-price products or low-price products of the same model; the product title preference includes, but is not limited to: preference for long titles or short titles; the logistics preference includes, but is not limited to, one or more of the following: preference for low-price logistics, preference for high-efficiency logistics, etc.; the delivery cycle preference includes, but is not limited to: preference for short shipping cycles, insensitive to shipping cycles, etc.

[0045] In some alternative embodiments, obtaining the user preference information matched by the user based on the user historical data associated with the user information includes: obtaining the user historical data associated with the user information, where the user historical data includes one or more of the following: shipping address, product price, order logistics information, title of the published product, product delivery cycle; using a preset large model to reason about the user historical data associated with the user information to obtain the user preference information matched by the user. In some other alternative embodiments, a pre-trained neural network model can also be used to identify the user historical data associated with the user information to obtain the user preference information matched by the user.

[0046] For the specific implementation of obtaining the user preference information matched by the user based on the user historical data associated with the user information, refer to the prior art and will not be elaborated in the embodiments of the present application.

[0047] Sub-step S4: Generate the preset release information of the target product based on the first product information, the second product information, and the user preference information.

[0048] Optionally, generating the preset release information of the target product based on the first product information, the second product information, and the user preference information includes: using the second product information as the basic data, using the first product information as a supplement, and generating the preset release information of the target product according to the user preference information.

[0049] In the embodiments of the present application, after obtaining the product information input by the user, the user preference information obtained by analysis, and the product information of the popular products of the same model of the target product obtained by searching, further analyze these three types of data, and synthesize these three types of data to understand the intention of the user (such as a merchant), and form product information of high-quality products that conforms to the user's habits, matches the user input, and includes the elements of popular products as the preset release information. Among them, the preset release information includes, but is not limited to, one or more of the following information: product title, keywords, attributes, pictures, detailed descriptions, prices, categories, logistics and other core elements of the product.

[0050] Optionally, using the second product information as the basic data and the first product information as a supplement, generate the information to be released of the target product according to the user preference information, including: generating a prompt word based on the first product information, the second product information and the user preference information, and the prompt word is used to guide the preset large model to use the second product information as the basic data, the first product information as a supplement, and generate the preset release information of the target product according to the user preference information. Optionally, different preset release information is generated by using different preset large models, so as to improve the quality of the generated preset release information.

[0051] Taking the scenario of a merchant user publishing a product as an example, using the data of the same model of high-quality products as the basis, and extracting the core competitiveness data therein (such as: keywords, prices, delivery cycles, etc.); on the other hand, using the user input data (i.e., the first product information) as a supplement, and using the characteristic data that is reflected in the user input data but not in the data of the same model of high-quality products as the supplementary product information; at the same time, referring to the merchant preference data for adjustment, expressing the merchant preference data (i.e., the user preference information) (such as attribute information such as the place of shipment), so that the preset generative large model adjusts the data according to the merchant preference (such as if the merchant prefers a long title, then expand the title again; if the prices of the same model of the merchant are generally low, then reduce the price of the generated data, etc.).

[0052] After the server combines the user input, the product information of the same model of popular products across the network, and the user preference information to generate the preset release information of the target product, the execution result can be fed back to the client. Among them, the execution result includes, but is not limited to, one or more of the following: whether the product information is successfully generated, the generated preset release information, etc.

[0053] Step 106, use the preset release information as the initial value of the corresponding information to be released, and initialize the release configuration interface.

[0054] After determining that the preset release information is successfully generated according to the execution result, the client can initialize the corresponding release information of the target product based on the preset release information, and display a release configuration interface for the product release information on the client based on the initialization result, so as to allow the user to modify and supplement the release information of the target product.

[0055] For example, the client initializes the corresponding release information of the target product in the release configuration interface respectively based on the core elements of the product such as the product title, keywords, attributes, pictures, detailed descriptions, prices, categories, logistics, etc. generated by the server. On the other hand, the client initializes other release information of the target product in the release configuration interface with preset default values, and displays the initial release configuration interface based on the initialized release information.

[0056] Among them, the types and initial values of the configurable release information displayed in the release configuration interface are determined according to the category of the target product. For example, products in different categories have different attributes, so products in different categories need to configure different attributes. Taking the target product as an LED lamp as an example, in addition to the product title, keywords, pictures, detailed descriptions, selling points, and some attributes generated in the previous steps, its release information can also configure attributes such as the material, color temperature, control method, light source, input voltage, and power of the product. Another example is that taking the target product as a handbag as an example, in addition to the product title, keywords, pictures, detailed descriptions, selling points, and some attributes generated in the previous steps, its release information can also configure attributes such as the material, color, style, volume, and weight of the product.

[0057] In the embodiments of the present application, the types of the configurable release information displayed in the release configuration interface are not limited.

[0058] In some optional embodiments, the release configuration interface is further used to configure: the target release platform and / or custom release rules.

[0059] In some optional embodiments, a release platform selection box is provided in the release configuration interface of the client. Each option in the release platform selection box corresponds to a release platform that opens a product release interface to the product release system (such as a mainstream commodity release website). The user can select the release platform for the product that the current user is about to release through the options in the release platform selection box, that is, the target release platform. Taking the product release system as an example of establishing product release authorizations with M (M is a positive integer greater than 1) release platforms in advance, the release platform selection box includes M options, and each option corresponds to a release platform.

[0060] In some other alternative embodiments, a custom publishing rule configuration area is further set on the publishing configuration interface of the client. The user can configure the target publishing platform and / or customize the product publishing rules through the custom publishing rule configuration area to adapt to more publishing platforms.

[0061] Step 108, in response to the publishing configuration operation performed on the publishing configuration interface, obtain the information to be published, the target publishing platform, and / or the customized product publishing rules configured by the user.

[0062] On the publishing configuration interface, the user can modify the information to be published generated in the previous step, or can also configure other product information required for publishing the target product, such as the target country, transaction information, delivery period, logistics provider, product service, price, inventory, product specifications, etc.

[0063] Optionally, a confirmation configuration button is set on the publishing configuration interface. The user can trigger the confirmation configuration button to perform the operation of publishing the target product based on the information to be published currently displayed on the publishing configuration interface. After the client detects that the confirmation configuration button is triggered, it obtains the information to be published currently displayed on the publishing configuration interface as the information to be published configured by the user.

[0064] Step 110, send a product publishing request to the server, so that the server performs the product publishing operation of the target product based on the information to be published, the target publishing platform, and / or the customized product publishing rules configured by the user.

[0065] After that, the client generates a product publishing request based on the information to be published configured by the user and sends the product publishing request to the server. Optionally, the product publishing request carries the information to be published configured by the user.

[0066] After receiving the product publishing request, the server can obtain the information to be published configured by the user by parsing the product publishing request. Then, the server performs the product publishing operation of the target product based on the information to be published, the target publishing platform, and / or the customized product publishing rules configured by the user.

[0067] In a specific application scenario, taking the generation of product pictures for an e-commerce website as an example, some websites need to generate white-background pictures, some websites need to generate product pictures with backgrounds, some products need to be published on English websites, some products need to be published on websites in other languages, some products are priced in US dollars on the target publishing platforms, and some products are priced in RMB on the target publishing platforms. That is, the attributes, categories, etc. of the same product are different on different publishing platforms. Based on this, optionally, after obtaining the information to be published of the target product, the information to be published is further adjusted and optimized based on the characteristics of the target publishing platform to obtain product information that conforms to the platform specifications and habits of the target publishing platform.

[0068] Optionally, the product publishing operation of the target product based on the information to be published configured by the user, the target publishing platform, and / or the custom product publishing rules includes: obtaining the general product publishing rules of the target publishing platform; integrating the general product publishing rules and the custom product publishing rules to obtain the product publishing rules of the target publishing platform; based on the product publishing rules, performing adaptive processing on the information to be published configured by the user to generate the publishing information of the target product; and publishing the target product to the target publishing platform based on the publishing information.

[0069] Among them, the adaptive processing based on the product publishing rules includes one or more of the following: performing case normalization on the product title in the information to be published, performing mapping processing on the attribute information in the information to be published, performing category mapping on the category information in the information to be published, performing language translation processing on the information to be published, and performing currency conversion on the specified information in the information to be published.

[0070] Among them, the general product publishing rules corresponding to the target publishing platform can be obtained through the product publishing interface opened by the target platform.

[0071] Optionally, integrating the general product publishing rules and the custom product publishing rules to obtain the product publishing rules of the target publishing platform includes: using the custom product publishing rules as the preferred rules to supplement the general product publishing rules to obtain the product publishing rules of the target publishing platform.

[0072] Optionally, based on the product publishing rules, performing adaptive processing on the information to be published configured by the user to generate the publishing information of the target product includes: using a preset optimization large model to perform adaptive processing on the information to be published configured by the user based on the product publishing rules to generate the publishing information of the target product.

[0073] In some alternative embodiments, a pre-set generative large model may be adopted to process the information to be released, and generate the information to be released for the target product. For example, using the information to be released as input data, a prompt is generated based on the product information mapping relationship between the product information model of the product release system and the product release rules. The prompt is used to guide the pre-set generative large model to generate product information that complies with the product release rules based on the input data, and the generated product information is used as the release information of the target product.

[0074] Specifically, taking the information to be released of the target product as input, the multi-round conversation ability of the pre-set generative large model is called, and according to the product release rules, the category of the target product on the target release platform is predicted step by step. For another example, taking the information to be released of the target product as input, the translation ability of the pre-set generative large model is called, and according to the product release rules, the information to be released is translated into the language required by the target release platform.

[0075] Among them, for the specific implementation manners of the above various information processing operations, reference may be made to the prior art, and details are not described in the embodiments of the present application.

[0076] In some alternative embodiments, before sending a product release request to the server, it further includes: sending an authorization request to the server, where the authorization request is used to trigger the server to authorize the product release operation of the user on the target release platform.

[0077] For example, after the client detects a product release operation performed by the user, an authorization request is first generated based on the user information and sent to a pre-set server. Among them, the authorization request at least includes: the user identification information of the user and the platform information of the target release platform.

[0078] After receiving the authorization request, the server calls the authorization interface opened by the target release platform with the user identification information as a parameter, triggering the target release platform to authorize the product release operation of the user.

[0079] For the specific implementation manners of sending an authorization request to the server based on the user operation and the server authorizing the product release operation of the user on the target release platform, reference may be made to the prior art, and details are not described in the embodiments of the present application.

[0080] In the case of successful authorization, a product release request is sent to the server. In the case of failed authorization, the product release operation is not performed.

[0081] In some alternative embodiments, generating the preset release information of the target product based on the user's user information and the data source includes: using corresponding preset large models to respectively generate the preset release information of the target product based on the user's user information and the data source, and respectively collecting the first preset evaluation indicators when each of the preset large models generates the preset release information of the target product. After sending the product release request to the server, the product release request is used to trigger the server to perform the product release operation of the target product based on the information to be released configured by the user, the target release platform, and / or the custom product release rule. After that, the method further includes: collecting the second preset evaluation indicators of the target product based on the product release operation; and outputting the first preset evaluation indicators and the first preset evaluation indicators as large model optimization reference data.

[0082] Among them, the first preset evaluation indicators include, but are not limited to, one or more of the following: success rate, response time. In the embodiments of the present application, different preset large models can be used to generate different preset release information respectively, or the same preset large model can be used to generate different preset release information. For example, a first preset large model can be used to generate a product title, a second preset large model can be used to generate a product category, a third preset large model can be used to generate a detailed description, a fourth preset large model can be used to generate keywords, a fifth preset large model can be used to generate product pictures, etc. Another example is that one preset large model can be used to generate text-based preset release information, and another preset large model can be used to generate product pictures.

[0083] In some alternative embodiments, a monitoring program can be set to detect the execution time and results of using different preset large models to respectively generate the corresponding preset release information of the target product based on the user's user information and the data source, so as to obtain the duration consumed by the preset large model to generate the corresponding product information, the generated results, that is, the error information output, etc. Among them, the generated results include, but are not limited to, any one of the following: generation failure, successfully generated product information. Further, the response time of the corresponding preset large model is determined according to the consumed duration, and the success rate of the corresponding preset large model is obtained according to the generated results.

[0084] In some other alternative embodiments, other methods can also be used to collect the first preset evaluation indicators when each of the preset large models generates the preset release information of the target product. The present application does not limit the specific implementation manner of collecting the first preset evaluation indicators when each of the preset large models generates the preset release information of the target product.

[0085] Optionally, the second preset evaluation indicators include, but are not limited to, one or more of the following: the adoption rate, release rate, error rate, and effect of the released product of the preset release information.

[0086] Optionally, by analyzing the similarities and differences between the product information (i.e., the released information) released through the said release operation and the preset release information of each said target product, the adoption rate of the preset release information is obtained. For example, by counting the similarities and differences between the product names generated by a preset large model for a number of target products and the finally released product names, the probability of the content generated by the preset large model for generating product names being adopted can be obtained. If the two are different, it means that the content generated by the preset large model is not adopted. If they are exactly the same, it means that the content generated by the preset large model is adopted. According to this method, the probability of the content generated by the preset large model being adopted is obtained as the adoption rate of the preset release information.

[0087] Optionally, the release rate of the preset release information is used to represent the proportion of product release operations performed on the already generated preset release information. The release rate of the preset release information can be obtained by counting the number of product release requests and product information generation requests.

[0088] The error rate of the preset release information is used to represent the probability of the preset large model outputting an incorrect result during the information extraction and release information generation process. The error rate of the preset release information can be obtained through statistical analysis by analyzing the output results of the preset large model.

[0089] The effect of the released product is used to represent the user satisfaction of the already released product. The effect of the released product of the preset release information can be obtained by collecting and analyzing the online user feedback of the already released target products.

[0090] The above has exemplarily described the acquisition methods of various second preset evaluation indicators. In specific implementation, other methods can also be used to collect the second preset evaluation indicators of the target products. In the embodiments of the present application, the specific implementation manners of collecting the second preset evaluation indicators of the target products based on the said product release operation are not limited.

[0091] After obtaining the first preset evaluation indicator and the first preset evaluation indicator, the first preset evaluation indicator and the first preset evaluation indicator can be output on the client for use as reference data for optimizing each preset large model, realizing multi-dimensional and multi-link data collection and feedback of all preset large models, thereby improving the accuracy and performance of the preset large model in generating product information. For example, for the preset release information with a high adoption rate, the generation strategy of the preset large model that generates this preset release information can be unchanged, while for the preset release information with a low adoption rate, the generation strategy of the preset large model that generates this preset release information needs to be optimized.

[0092] In summary, in the product release method disclosed in the embodiments of the present application, by obtaining the data source for releasing the target product input by the user on the client side and sending a product information generation request to the server side, the server side generates the preset release information of the target product based on the user information of the user and the data source, so that the generated product release information is more matched with the user preferences; then, the client uses the preset release information as the initial value of the corresponding information to be released, initializes the release configuration interface, and in response to the release configuration operation performed on the release configuration interface, obtains the information to be released, the target release platform and / or the custom product release rules configured by the user; then, sends a product release request to the server side, so that the server side performs the product release operation of the target product based on the information to be released, the target release platform and / or the custom product release rules configured by the user, realizing fully automated and fast product release, and effectively improving the product release efficiency. Moreover, by displaying the release configuration interface, the user can modify or supplement the configuration of the product release information automatically extracted from the input data source, which helps to expand the target platform adaptation ability of the products released by this method and meet the personalized product release requirements, making the released products more matched with the user needs.

[0093] Taking the application scenario of releasing products on an e-commerce website as an example, in the prior art, when releasing products, merchants need to conduct their own research on high-quality products in the current industry and extract the key information therein. Merchants need to prepare data by themselves, including title formulation, detailed description design, picture generation, etc., and fill in forms and a series of operations. Moreover, the quality of product release is related to the capabilities of the merchants. According to statistics, currently, the average time for releasing a product on several major e-commerce platforms is about 10 minutes, and the product release efficiency is relatively low; in terms of the quality of product release, the number of newly released products accounts for 32.8%, and the proportion of page view volume of the detailed pages is only 9.2%. The overall quality of new products needs to be further improved. By using the product release method disclosed in the embodiments of the present application, based on various forms of materials of the product to be released input by the merchant, and based on the text understanding and generation technology of artificial intelligence, combined with the merchant's preferences and the product information of the same type of high-quality products on the global sites, the release information of the product is automatically generated to realize one-key product release, which not only improves the product release efficiency but also improves the product release quality.

[0094] On the other hand, by optimizing and adjusting the product release information in a fully automated manner according to the platform release rules of the target release platform and the custom release rules of the user, the product release information is made more suitable for the target platform rules and habits, so that the product release method is adapted to multiple platforms, achieving one-key release of single products across the whole platform and greatly improving the product release efficiency.

[0095] Furthermore, by combining user input information, high-quality same-model product information from multiple sites, and user preference information to generate product release information, it helps to produce high-quality products that conform to user habits, match user input, and contain popular product elements, assisting seller users in quickly tracking industry hotspots and generating high-quality products.

[0096] Based on the above embodiments, this embodiment further provides a product release method applied to a server side, as Figure 3 shown, the method includes: steps 302 to 310.

[0097] Step 302, in response to a product information generation request sent by a client, obtain the user information carried in the product information generation request and the data source for releasing a target product.

[0098] For the specific implementation manner in which the client generates and sends a product information generation request, refer to the relevant descriptions in the previous embodiments, which will not be elaborated here.

[0099] After the server side receives the product information generation request sent by the client, by parsing the product information generation request, obtain the user information carried in the product information generation request and the data source for releasing a target product.

[0100] Optionally, the data source includes one or more of the following: product description text, product pictures, product links, product material compression packages.

[0101] Step 304, based on the user information and the data source, generate a preset release information for the target product.

[0102] Optionally, generating a preset release information for the target product based on the user information and the data source includes: performing product information analysis and processing on the data source to obtain first product information input by the user for the target product; based on the first product information, obtain second product information of the same-model popular product of the target product; based on the user historical data associated with the user information, obtain user preference information; based on the first product information, the second product information, and the user preference information, generate a preset release information for the target product.

[0103] Among them, the preset release information includes but is not limited to one or more of the following information: product title, keywords, attributes, pictures, detailed descriptions, prices, categories, logistics, and other core product elements.

[0104] In the specific implementation process, the server side sequentially calls a preset large model to generate each preset release information.

[0105] For the specific implementation of each step in which the server generates the preset release information of the target product based on the user information and the data source, refer to the relevant descriptions in the foregoing embodiments, which will not be elaborated herein.

[0106] Step 306: Send the preset release information to the client, so that the client initializes the release configuration interface with the preset release information as the initial value of the corresponding information to be released, and obtains the information to be released, the target release platform, and / or the custom product release rules configured by the user in response to the release configuration operation performed on the release configuration interface.

[0107] After the server generates the preset release information of the target product, it sends the preset release information to the client, which is output and displayed on the release configuration interface for the user to confirm and modify. On the other hand, configuration boxes for other release information of the target product are also displayed on the release configuration interface for the user to configure the information to be released, the target release platform, and / or the custom release rules of the target product.

[0108] For the specific implementation of the client obtaining the information to be released, the target release platform, and / or the custom product release rules configured by the user in response to the release configuration operation performed on the release configuration interface, refer to the relevant descriptions in the foregoing embodiments, which will not be elaborated herein.

[0109] Step 308: In response to the product release request sent by the client, obtain the information to be released, the target release platform, and / or the custom product release rules configured by the user carried in the product release request.

[0110] For the specific implementation of the client generating and sending the product release request, refer to the relevant descriptions in the foregoing embodiments, which will not be elaborated herein.

[0111] After the server receives the product release request sent by the client, it parses the product release request to obtain the information to be released, the target release platform, and / or the custom product release rules configured by the user carried in the product release request.

[0112] Step 310: Perform the product release operation of the target product based on the information to be released, the target release platform, and / or the custom product release rules configured by the user.

[0113] For the specific implementation of performing the product release operation of the target product based on the information to be released, the target release platform, and / or the custom product release rules configured by the user, refer to the relevant descriptions in the foregoing embodiments, which will not be elaborated herein.

[0114] So far, triggered by the client, based on the data source of the target product input by the client and the user information of the current user, the publishing operation of the target product has been completed.

[0115] In some alternative embodiments, generating the preset publishing information of the target product based on the user information and the data source includes: using corresponding preset large models to generate the preset publishing information of the target product based on the user information and the data source respectively, and collecting the first preset evaluation indicators when each of the preset large models generates the preset publishing information of the target product; after performing the product publishing operation of the target product based on the to-be-published information configured by the user, the target publishing platform, and / or the custom product publishing rules, the method further includes: collecting the second preset evaluation indicators of the target product based on the product publishing operation; sending the first preset evaluation indicators and the first preset evaluation indicators as large model optimization reference data to the client, and displaying the first preset evaluation indicators and the first preset evaluation indicators by the client.

[0116] The user can optimize the information extraction strategy, information generation strategy, etc. of each of the above-mentioned preset large models according to the first preset evaluation indicators and the first preset evaluation indicators displayed by the client, and fine-tune the corresponding preset large models based on the optimized strategies, so as to improve the accuracy of information extraction and generation, and further improve the product publishing efficiency and the quality of the published products.

[0117] In summary, for the product publishing method disclosed in the embodiments of the present application, the server responds to the product information generation request sent by the client, obtains the user information carried in the product information generation request and the data source for publishing the target product, and then, based on the user information and the data source, generates the preset publishing information of the target product, making the generated product publishing information more in line with the user preferences; sends the preset publishing information to the client, so that the client uses the preset publishing information as the initial value of the corresponding to-be-published information to initialize the publishing configuration interface, and responds to the publishing configuration operation performed on the publishing configuration interface to obtain the to-be-published information configured by the user, the target publishing platform, and / or the custom product publishing rules; finally, the server responds to the product publishing request sent by the client, obtains the to-be-published information configured by the user, the target publishing platform, and / or the custom product publishing rules carried in the product publishing request; performs the product publishing operation of the target product based on the to-be-published information configured by the user, the target publishing platform, and / or the custom product publishing rules. This method realizes fully automated and fast product publishing, effectively improving the product publishing efficiency.

[0118] On the other hand, by following the platform release rules of the target release platform and user-defined release rules, product release information is optimized and adjusted in a fully automated manner, making the product release information more suitable for the target platform rules and habits, thereby making the product release method adaptable to multiple platforms and achieving one-click release of single products on all platforms, greatly improving product release efficiency.

[0119] Furthermore, by combining user input information, multi-site high-quality product information and user preference information to generate product release information, it is helpful to produce high-quality products that conform to user habits, match user input, and contain popular elements, helping sellers and users quickly track industry hotspots and generate high-quality products.

[0120] Based on the above embodiment, this embodiment also provides a product release system, such as Figure 4 As shown, the system includes a client and a server. The execution process of the product release system is as follows: Step 402, the client obtains the data source input by the user for publishing the target product; Step 404: the client sends a product information generation request to the server, wherein the product information generation request carries the user information of the user and the data source; Step 406, the server generates preset release information of the target product in response to the product information generation request based on the user information and the data source carried in the product information generation request; Step 408, the server sends the preset publishing information to the client; Step 410, the client uses the preset publishing information as the initial value of the corresponding information to be published, and initializes the publishing configuration interface; Step 412, the client obtains the to-be-published information, target publishing platform and / or customized product publishing rules configured by the user in response to the publishing configuration operation performed on the publishing configuration interface; Step 414, the client sends a product publishing request to the server, wherein the product publishing request carries the information to be published, the target publishing platform and / or the customized product publishing rules configured by the user; Step 416, the server side responds to the product release request and obtains the user-configured information to be released, the target release platform and / or the custom product release rule carried in the product release request; Step 418: The server performs a product publishing operation of the target product based on the to-be-published information configured by the user, the target publishing platform and / or the custom product publishing rule.

[0121] For the specific implementation manners of the above steps executed by the client and the server, refer to the relevant descriptions in the foregoing embodiments, and details are not described herein again.

[0122] In summary, for the product release system disclosed in the embodiments of the present application, by obtaining, at the client, the data source for releasing the target product input by the user and sending a product information generation request to the server, then, based on the user information of the user and the data source, the server generates the preset release information of the target product, making the generated product release information more in line with the user's preferences; afterwards, the client uses the preset release information as the initial value of the corresponding information to be released, initializes the release configuration interface, and in response to the release configuration operation performed on the release configuration interface, obtains the information to be released, the target release platform and / or the custom product release rule configured by the user; then, sends a product release request to the server, so that the server performs the product release operation of the target product based on the information to be released, the target release platform and / or the custom product release rule configured by the user, realizing fully automated and fast product release, and effectively improving the product release efficiency.

[0123] On the other hand, by optimizing and adjusting the product release information in a fully automated manner according to the platform release rules of the target release platform and the release rules customized by the user, the product release information is made more suitable for the target platform rules and habits, so that the product release method is adapted to multiple platforms, achieving one-key release of single products across all platforms and greatly improving the product release efficiency.

[0124] Based on the above embodiments, the present embodiment further provides a product release device, which is applied to the client. The device includes: An input information acquisition module, configured to acquire the data source for releasing the target product input by the user; A preset release information generation module, configured to send a product information generation request to the server, so that the server generates the preset release information of the target product based on the user information of the user and the data source; A release configuration interface display module, configured to initialize the release configuration interface with the preset release information as the initial value of the corresponding information to be released; A release configuration acquisition module, configured to acquire the information to be released, the target release platform and / or the custom product release rule configured by the user in response to the release configuration operation performed on the release configuration interface; A product release module, configured to send a product release request to the server, so that the server performs the product release operation of the target product based on the information to be released, the target release platform and / or the custom product release rule configured by the user.

[0125] Optionally, generating the preset release information of the target product based on the user information of the user and the data source includes: Performing product information analysis and processing on the data source to obtain first product information input by the user of the target product; Based on the first product information, obtaining second product information of the popular product of the same model as the target product; Based on the user historical data associated with the user information, obtaining user preference information matched by the user; Based on the first product information, the second product information, and the user preference information, generating the preset release information of the target product.

[0126] Optionally, performing product information analysis and processing on the data source to obtain first product information input by the user of the target product includes: Performing product information analysis and processing operations on the data source to obtain text and / or pictures input by the user; Invoking a preset large model to perform information extraction and generation processing on the text and / or pictures to obtain first product information input by the user of the target product, where the first product information includes one or more of the following: category, title, picture, keyword, description, logistics, attribute.

[0127] Optionally, based on the first product information, obtaining second product information of the popular product of the same model as the target product includes: Performing encoding processing on one or more of the first product information to obtain an encoding vector of the target product; Adopting a vector matching method to recall products to be matched from a preset product library based on the encoding vector; Using a preset large model to perform correlation scoring on the target product and the products to be matched based on the first product information, and screening out the products of the same model as the target product from the products to be matched according to the scoring results; Based on preset quality evaluation indicators, screening the products of the same model to obtain the popular products of the same model as the target product; Obtaining the preset product information of the popular product of the same model as the second product information.

[0128] Optionally, based on the first product information, the second product information, and the user preference information, generating the preset release information of the target product includes: Using the second product information as the basic data, using the first product information as a supplement, and generating the preset release information of the target product according to the user preference information.

[0129] Optionally, performing the product release operation of the target product based on the to-be-released information configured by the user, the target release platform, and / or the custom product release rule includes: Obtaining the general product release rule of the target release platform; Integrating the general product release rule and the custom product release rule to obtain the product release rule of the target release platform; Performing adaptive processing on the to-be-released information configured by the user based on the product release rule to generate the release information of the target product; Releasing the target product to the target release platform based on the release information.

[0130] Optionally, the data source includes one or more of the following: product description text, product picture, product link, product material compression package.

[0131] Optionally, generating the preset release information of the target product based on the user information of the user and the data source includes: Using the corresponding preset large model to respectively generate the preset release information of the target product based on the user information of the user and the data source, and respectively collecting the first preset evaluation index when each preset large model generates the preset release information of the target product; After sending the product release request to the server, so that the server performs the product release operation of the target product based on the to-be-released information configured by the user, the target release platform, and / or the custom product release rule, the device further includes: A feedback data collection module, configured to collect the second preset evaluation index of the target product based on the product release operation; A feedback data output module, configured to output the first preset evaluation index and the first preset evaluation index as large model optimization reference data.

[0132] The product release device disclosed in the embodiments of the present application is used to implement the above product release method. For the specific implementation manners of the modules of the device, refer to the specific implementation manners of the corresponding steps in the foregoing method embodiments, which will not be elaborated herein.

[0133] In summary, the product release device disclosed in the embodiments of the present application obtains, on the client side, the data source for releasing the target product input by the user, and sends a product information generation request to the server side, so that the server side generates the preset release information of the target product based on the user information of the user and the data source, making the generated product release information more in line with the user's preferences; then, the client uses the preset release information as the initial value of the corresponding information to be released, initializes the release configuration interface, and in response to the release configuration operation performed on the release configuration interface, obtains the information to be released configured by the user, the target release platform, and / or the custom product release rule; and then sends a product release request to the server side, so that the server side performs the product release operation of the target product based on the information to be released configured by the user, the target release platform, and / or the custom product release rule, realizing fully automated and fast product release and effectively improving the product release efficiency.

[0134] On the other hand, by optimizing and adjusting the product release information in a fully automated manner according to the platform release rules of the target release platform and the release rules customized by the user, the product release information is made more suitable for the target platform rules and habits, so that the product release method is adapted to multiple platforms, achieving one-key release of single products across the entire platform and greatly improving the product release efficiency.

[0135] Furthermore, by combining the user input information, the high-quality same-style product information of multiple sites, and the user preference information to generate the product release information, it helps to produce high-quality products that conform to the user's habits, match the user input, and contain popular product elements, assisting seller users in quickly tracking industry hotspots and generating high-quality products.

[0136] Based on the above embodiments, this embodiment further provides a product release device, which is applied to the server side. The device includes: A user input acquisition module, configured to acquire the user information carried in the product information generation request and the data source for releasing the target product in response to the product information generation request sent by the client; A preset release information generation module, configured to generate the preset release information of the target product based on the user information and the data source; A preset release information sending module, configured to send the preset release information to the client, so that the client uses the preset release information as the initial value of the corresponding information to be released, initializes the release configuration interface, and in response to the release configuration operation performed on the release configuration interface, obtains the information to be released configured by the user, the target release platform, and / or the custom product release rule; A release configuration information acquisition module, configured to obtain the to-be-released information configured by the user, the target release platform, and / or the custom product release rule carried in the product release request in response to a product release request sent by a client. A product release module, configured to perform a product release operation of the target product based on the to-be-released information configured by the user, the target release platform, and / or the custom product release rule.

[0137] Optionally, based on the user information and the data source, generate preset release information of the target product, including: Perform product information analysis and processing on the data source to obtain first product information input by the user of the target product; Based on the first product information, obtain second product information of the popular product of the same model of the target product; Based on the user historical data associated with the user information, obtain user preference information; Based on the first product information, the second product information, and the user preference information, generate preset release information of the target product.

[0138] The product release device disclosed in the embodiments of the present application is used to implement the above product release method. For the specific implementation manners of the modules of the device, refer to the specific implementation manners of the corresponding steps in the foregoing method embodiments, which will not be elaborated herein.

[0139] In summary, the product release device disclosed in the embodiments of the present application is such that the server responds to a product information generation request sent by a client, obtains the user information carried in the product information generation request and the data source for releasing the target product, and then, based on the user information and the data source, generates preset release information of the target product, making the generated product release information more matched with the user preferences; sends the preset release information to the client, enabling the client to use the preset release information as the initial value of the corresponding to-be-released information to initialize the release configuration interface, and in response to a release configuration operation performed on the release configuration interface, obtains the to-be-released information configured by the user, the target release platform, and / or the custom product release rule; finally, the server responds to a product release request sent by the client, obtains the to-be-released information configured by the user, the target release platform, and / or the custom product release rule carried in the product release request; and performs a product release operation of the target product based on the to-be-released information configured by the user, the target release platform, and / or the custom product release rule. This device realizes fully automated and fast product release, effectively improving the product release efficiency.

[0140] On the other hand, by following the platform release rules of the target release platform and user-defined release rules, product release information is optimized and adjusted in a fully automated manner, making the product release information more suitable for the target platform rules and habits, thereby making the product release method adaptable to multiple platforms and achieving one-click release of single products on all platforms, greatly improving product release efficiency.

[0141] Furthermore, by combining user input information, multi-site high-quality product information and user preference information to generate product release information, it is helpful to produce high-quality products that conform to user habits, match user input, and contain popular elements, helping sellers and users quickly track industry hotspots and generate high-quality products.

[0142] The embodiment of the present application further provides a non-volatile readable storage medium, in which one or more modules (programs) are stored. When the one or more modules are applied to a device, the device can execute instructions (instructions) of each method step in the embodiment of the present application.

[0143] The embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method described in the embodiment of the present application.

[0144] The embodiment of the present application also provides an electronic device, comprising: a processor, and a memory connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method described in the embodiment of the present application. In the embodiment of the present application, the electronic device includes a server, a terminal device, and other devices.

[0145] The embodiment of the present application further discloses a computer program product, including a computer program / computer executable instructions, which, when executed by a processor in an electronic device, implements the method described in the embodiment of the present application.

[0146] The embodiments of the present disclosure may be implemented as a device configured as desired using any appropriate hardware, firmware, software, or any combination thereof, and the device may include electronic devices such as a server (cluster), a terminal, etc. Figure 5 An exemplary apparatus 500 that can be used to implement various embodiments described in this application is schematically shown.

[0147] For one embodiment, Figure 5An exemplary device 500 is shown, which has one or more processors 502, a control module (chipset) 504 coupled to at least one of the (one or more) processors 502, a memory 506 coupled to the control module 504, a non-volatile memory (NVM) / storage device 508 coupled to the control module 504, one or more input / output devices 510 coupled to the control module 504, and a network interface 512 coupled to the control module 504.

[0148] The processor 502 may include one or more single-core or multi-core processors, and the processor 502 may include any combination of general-purpose processors or dedicated processors (such as graphics processors, application processors, baseband processors, etc.). In some embodiments, the device 500 can act as devices such as the server, terminal, etc. described in the embodiments of the present application.

[0149] In some embodiments, the device 500 may include one or more computer-readable media (such as the memory 506 or NVM / storage device 508) having instructions 514, and one or more processors 502 combined with the one or more computer-readable media and configured to execute the instructions 514 to implement modules so as to perform the actions described in the present disclosure.

[0150] For one embodiment, the control module 504 may include any suitable interface controller to provide any suitable interface to at least one of the (one or more) processors 502 and / or any suitable device or component communicating with the control module 504.

[0151] The control module 504 may include a memory controller module to provide an interface to the memory 506. The memory controller module can be a hardware module, a software module, and / or a firmware module.

[0152] The memory 506 can be used, for example, to load and store data and / or instructions 514 for the device 500. For one embodiment, the memory 506 may include any suitable volatile memory, such as suitable DRAM. In some embodiments, the memory 506 may include double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

[0153] For one embodiment, the control module 504 may include one or more input / output controllers to provide an interface to the NVM / storage device 508 and the (one or more) input / output devices 510.

[0154] For example, the NVM / storage device 508 can be used to store data and / or instructions 514. The NVM / storage device 508 can include any suitable non-volatile memory (e.g., flash memory) and / or can include any suitable (one or more) non-volatile storage devices (e.g., one or more hard disk drives (HDDs), one or more compact disc (CD) drives, and / or one or more digital versatile disc (DVD) drives).

[0155] The NVM / storage device 508 can include storage resources that are part of a device installed as device 500, or it can be accessible by the device without necessarily being part of the device. For example, the NVM / storage device 508 can be accessed via a network through (one or more) input / output devices 510.

[0156] (One or more) input / output devices 510 can provide an interface for device 500 to communicate with any other suitable devices. The input / output devices 510 can include communication components, audio components, sensor components, etc. The network interface 512 can provide an interface for device 500 to communicate through one or more networks. Device 500 can wirelessly communicate with one or more components of a wireless network according to any of one or more wireless network standards and / or protocols, such as accessing a wireless network based on communication standards like Bluetooth, WiFi, 2G, 3G, 4G, 5G, etc., or a combination thereof for wireless communication.

[0157] For one embodiment, at least one of (one or more) processors 502 can be logically encapsulated with one or more controllers of the control module 504 (e.g., a memory controller module). For one embodiment, at least one of (one or more) processors 502 can be logically encapsulated with one or more controllers of the control module 504 to form a system-in-package (SiP). For one embodiment, at least one of (one or more) processors 502 can be logically integrated with one or more controllers of the control module 504 on the same die. For one embodiment, at least one of (one or more) processors 502 can be logically integrated with one or more controllers of the control module 504 on the same die to form a system-on-chip (SoC).

[0158] In various embodiments, the device 500 may be, but is not limited to, a terminal device such as a server, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the device 500 may have more or fewer components and / or a different architecture. For example, in some embodiments, the device 500 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application specific integrated circuit (ASIC), and a speaker.

[0159] Among them, a main control chip can be used as a processor or a control module in the detection device. Sensor data, location information, etc. are stored in a memory or an NVM / storage device. The sensor group can be used as an input / output device, and the communication interface can include a network interface.

[0160] Embodiments of the present application also provide an electronic device, including: a processor; and a memory storing executable code thereon, which when executed, causes the processor to execute one or more of the methods as in the embodiments of the present application. In the embodiments of the present application, various data can be stored in the memory, such as target files, file-application association data, and other various data, and can also include user behavior data, etc., thereby providing a data basis for various processes.

[0161] Embodiments of the present application also provide one or more machine-readable media storing executable code thereon, which when executed, causes a processor to execute one or more of the methods as in the embodiments of the present application.

[0162] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments.

[0163] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, refer to each other.

[0164] Embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate for implementing in the process Figure 1one process or multiple processes and / or blocks Figure 1 means for the functions specified in one block or multiple blocks.

[0165] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions in the process Figure 1 one process or multiple processes and / or blocks Figure 1 specified in one block or multiple blocks.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing terminal device, such that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 specified in one block or multiple blocks.

[0167] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0168] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or terminal device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or terminal device including the said element.

[0169] The above has introduced in detail a product release method, a product release system, an electronic device, a storage medium, and a computer program product provided by the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A product publishing method, applied to a client, characterized in that: The method comprises: Obtain the data source input by the user for publishing the target product; Sending a product information generation request to a server, so that the server generates preset release information of the target product based on the user information of the user and the data source; Initialize the publishing configuration interface using the preset publishing information as the initial value of the corresponding information to be published; In response to the publishing configuration operation performed on the publishing configuration interface, obtaining the to-be-published information, the target publishing platform and / or the custom product publishing rules configured by the user; A product publishing request is sent to the server, so that the server performs a product publishing operation of the target product based on the to-be-published information configured by the user, the target publishing platform and / or the custom product publishing rule.

2. The method according to claim 1, characterized in that The generating the preset release information of the target product based on the user information of the user and the data source includes: Performing product information analysis processing on the data source to obtain first product information input by a user of the target product; Based on the first product information, obtain second product information of the same popular product as the target product; Based on the user history data associated with the user information, obtaining user preference information matched by the user; Based on the first product information, the second product information and the user preference information, preset release information of the target product is generated.

3. The method according to claim 2, characterized in that The performing product information analysis processing on the data source to obtain first product information input by a user of the target product includes: Perform product information analysis and processing operations on the data source to obtain text and / or pictures input by the user; The preset large model is called to extract and generate information from the text and / or image to obtain the first product information input by the user of the target product, wherein the first product information includes one or more of the following: category, title, image, keyword, description, logistics, and attributes.

4. The method according to claim 2, characterized in that: The acquiring, based on the first product information, second product information of the same popular product as the target product includes: Encoding one or more of the first product information to obtain a coding vector of the target product; Recalling the to-be-matched products from a preset product library based on the encoding vector in a vector matching manner; Using a preset large model to score the relevance of the target product and the to-be-matched product based on the first product information, and screening the to-be-matched products to obtain the same products as the target product from the to-be-matched products according to the scoring results; The same products are screened based on preset quality evaluation indicators to obtain the same popular products as the target product; The preset product information of the same popular product is obtained as the second product information.

5. The method according to claim 2, characterized in that: The generating the preset release information of the target product based on the first product information, the second product information and the user preference information includes: The second product information is used as basic data, the first product information is used as a supplement, and the preset release information of the target product is generated according to the user preference information.

6. The method according to claim 1, characterized in that The performing of the product publishing operation of the target product based on the to-be-published information configured by the user, the target publishing platform and / or the custom product publishing rule includes: Obtaining general product publishing rules of the target publishing platform; Combining the general product publishing rules and the custom product publishing rules to obtain product publishing rules for the target publishing platform; Based on the product release rules, adaptively process the information to be released configured by the user to generate release information of the target product; The target product is published to the target publishing platform based on the publishing information.

7. The method according to claim 1, characterized in that The data source includes one or more of the following: product description text, product image, product link, product material compressed package.

8. The method according to claim 1, characterized in that The generating the preset release information of the target product based on the user information of the user and the data source includes: Using the corresponding preset big model based on the user information of the user and the data source, respectively generate the preset release information of the target product, and respectively collect the first preset evaluation index when each of the preset big models generates the preset release information of the target product; After sending the product release request to the server so that the server performs the product release operation of the target product based on the to-be-released information configured by the user, the target release platform and / or the custom product release rule, the method further includes: Based on the product release operation, collecting a second preset evaluation index of the target product; The first preset evaluation index and the second preset evaluation index are output as large model optimization reference data.

9. A product publishing method, applied to a server, characterized in that: The method comprises: In response to a product information generation request sent by a client, obtaining user information carried in the product information generation request and a data source for publishing a target product; Based on the user information and the data source, generating preset release information of the target product; Sending the preset publishing information to the client, so that the client uses the preset publishing information as the initial value of the corresponding information to be published, initializes the publishing configuration interface, and obtains the information to be published, the target publishing platform and / or the custom product publishing rules configured by the user in response to the publishing configuration operation performed on the publishing configuration interface; In response to a product publishing request sent by a client, obtaining the user-configured information to be published, the target publishing platform and / or the custom product publishing rule carried in the product publishing request; A product publishing operation of the target product is performed based on the to-be-published information configured by the user, the target publishing platform and / or the custom product publishing rule.

10. The method according to claim 9, characterized in that Based on the user information and the data source, generating preset release information of the target product includes: Performing product information analysis processing on the data source to obtain first product information input by a user of the target product; Based on the first product information, obtain second product information of the same popular product as the target product; Acquiring user preference information based on user history data associated with the user information; Based on the first product information, the second product information and the user preference information, preset release information of the target product is generated.

11. A product release system, comprising: The client and the server are characterized by: The client is used to execute the method according to any one of claims 1 to 8; The server is used to execute the method according to claim 9 or 10.

12. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 10 when executed by a processor.

14. A computer program product comprising a computer program / computer executable instructions, characterized in that: When the computer program / computer executable instructions are executed by a processor in an electronic device, the method according to any one of claims 1 to 10 is implemented.

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